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DOE OSTI2024

The Hygrothermal Performance of Continuous and Cavity Wood Fiber Insulation

Wood fiber insulation is an emerging material known for its effective thermal performance and moisture management properties, making it a compelling alternative to conventional insulation. Its vapor permeability and ability to regulate indoor humidity contribute to improved building durability and comfort, particularly in varying climate conditions. The goal of this project is to characterize the hygrothermal performance of a new wood fiber insulation product line in U.S. climate zones and to facilitate the design and construction of the product in residential and light commercial building envelopes. This study investigates the thermal and hygrothermal performance of wood fiber insulation, both as continuous and cavity insulation. The research employs an exhaustive simulation task and an environmental chamber test of a wood fiber insulation. Over 400 simulations were conducted to study the hygrothermal characteristics of bio-based wood fiber insulation in various building envelope configurations across four climate zones. Environmental chamber tests were conducted under controlled winter conditions representative of Climate Zone 5A (Chicago, IL) to complement the simulation results. The chamber tests focused on the hygrothermal performance of the wood fiber insulation and the sheathing board to assess the insulation's ability to manage moisture in a cold climate. Simulation and environmental chamber test results were analyzed to evaluate the insulation's consistency and efficacy across diverse climatic zones. Results show that with a proper moisture control strategy, both cavity and continuous wood fiber insulation can work properly in both new and retrofit constructions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2020

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogenities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2014

On the importance of FIB-SEM specific segmentation algorithms for porous media

A new algorithmic approach to segmentation of highly porous three dimensional image data gained by focused ion beam tomography is described which extends the key-principle of local threshold backpropagation described in Salzer et al. (2012). The technique of focused ion beam tomography has shown to be capable of imaging the microstructure of functional materials. In order to perform a quantitative analysis on the corresponding microstructure a segmentation task needs to be performed. However, algorithmic segmentation of images obtained with focused ion beam tomography is a challenging problem for highly porous materials if filling the pore phase, e.g. with epoxy resin, is difficult. The gray intensities of individual voxels are not sufficient to determine the phase represented by them and usual thresholding methods are not applicable. We thus propose a new approach to segmentation that pays respect to the specifics of the imaging process of focused ion beam tomography. As an application of our approach, the segmentation of three dimensional images for a cathode material used in polymer electrolyte membrane fuel cells is discussed. We show that our approach preserves significantly more of the original nanostructure than a thresholding approach. - Highlights: • We describe a new approach to the segmentation of FIB-SEM images of porous media. • The first and last occurrences of structures are detected by analysing the z-profiles. • The algorithm is validated by comparing it to a manual segmentation. • The new approach shows significantly less artifacts than a thresholding approach. • A structural analysis also shows improved results for the obtained microstructure.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Micro X-ray computed tomography examination of mini plate fuel with hot isostatic pressed aluminum cladding

In order to minimize proliferation risks and improve security of nuclear material, the United States high performance research and test reactors (USHPRR) program is tasked with converting nuclear reactors that are fueled with highly enriched uranium (HEU) fuels to operate with low-enriched uranium (LEU) nuclear fuels. One favorable LEU fuel configuration is plate fuel with a metallic uranium-molybdenum foil clad within an aluminum alloy (AA 6061). In this fuel, the aluminum cladding is bonded with a hot isostatic pressing (HIP) method to seal the cladding around the fuel meat. However, the HIP process parameters influence the cladding performance, as a defective or incomplete bond can cause a pathway for corrosion. Micro X-ray computed tomography (XCT), a nondestructive technique that provides volumetric imaging, can be applied to inspect fuel plate cladding at the engineering scale. In this work, XCT methodology was developed and successfully utilized to not only observe the bond line of unirradiated mini fuel plates, but to also identify subsurface abnormalities in the plates’ cladding. Importantly, in future work, this technique could be applied to fuel plates, pre- and post-irradiation, to quantify irradiation effects on cladding defects and bond line integrity.

36 MATERIALS SCIENCE↗

DOE OSTI2020

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2025

Advanced Materials & Manufacturing Technology (AMMT): Process understanding for qualifying LPBF 316H SS

Investigations were conducted in fiscal years (FY) 2023 and 2024 to gather relevant data sets addressing challenges related to qualifying 316H stainless steel (SS) for use in future nuclear reactors. This work was a collaborative effort involving researchers from Idaho National Laboratory (INL), Argonne National Laboratory (ANL), and Oak Ridge National Laboratory (ORNL). Key outcomes included: • Development of process-structure-property data sets to better understand the relationships between manufacturing processes, material structure, and performance characteristics. • Establishment of an in-situ monitoring system to link these various data sets together. • Detailed characterization of the raw material feedstock to further strengthen the understanding of process-structure and process-property relationships. Building on this foundation, in FY25 there was interest in exploring the behavior of additively manufactured 316H SS under different test conditions to support the overall material qualification process. Los Alamos National Laboratory (LANL) was tasked with providing specimen samples to the collaborating labs, who then conducted a round-robin study examining factors like selective heat treatment, low-cycle fatigue (LCF), and tensile-creep (T-C) properties. Additionally, high-temperature differential scanning calorimetry (DSC) was performed by LANL to better understand how the material's thermal characteristics change as it is heated up to the melting point. This provided a more comprehensive understanding of the material's behavior. The combined results from these investigations could potentially be used to support the inclusion of 316H stainless steel in Section III, Division 5 of the relevant codes and standards, allowing its use in future nuclear reactor applications.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Enhanced resistance to helium irradiations through unusual interaction between high-entropy-alloy and helium

Finding high performance plasma-facing materials (PFMs) is one of the most important and challenging tasks for realizing the commercial application of fusion reactors. Herein, we found the CrMoTaWV high entropy alloy (HEA) is highly resistant to low-energy and high-flux He plasma exposure. The nanochannel HEA film has 20 times higher initial fluence for the formation of fuzz and a remarkable 8.9 times slower fuzz growth rate than those of W. Combining the in-situ TEM observation and the Molecular dynamics (MD) simulation of the He bubble growth process, a new mechanism for the enhanced radiation resistance in HEA with the unusual interaction between HEA and He is found, where, differing from traditional metal, bubble growth in HEA leads to non-directional emission of interstitial atoms while HEA greatly suppress the growth of He bubbles. Additionally, the special nanochannel structure further rise the radiation resistance through releasing He out of the HEA film and reducing the He concentration. This new nanochannel refractory HEA material presents a promising choice as the PFMs with excellent performance and a much longer serving lifetime for future commercial fusion reactors.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Determination of filter pore size for use in HB line phase II production of plutonium oxide

H-Canyon and HB-Line are tasked with the production of plutonium oxide (PuO 2 ) from a feed of plutonium (Pu) metal. The PuO 2 will provide feed material for the Mixed Oxide (MOX) Fuel Fabrication Facility. After dissolution of the Pu metal in H-Canyon, plans are to transfer the solution to HB-Line for purification by anion exchange. Anion exchange will be followed by plutonium(IV) oxalate precipitation, filtration, and calcination to form PuO 2 . The filtrate solutions, remaining after precipitation, contain low levels of Pu ions, oxalate ions, and may include solids. These solutions are transferred to H-Canyon for disposition. To mitigate the criticality concern of Pu solids in a Canyon tank, past processes have used oxalate destruction or have pre-filled the Canyon tank with a neutron poison. The installation of a filter on the process lines from the HB-Line filtrate tanks to H-Canyon Tank 9.6 is proposed to remove plutonium oxalate solids. This report describes SRNL’s efforts to determine the appropriate pore size for the filters needed to perform this function. Information provided in this report aids in developing the control strategies for solids in the process.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Gradient twinned 304 stainless steels for high strength and high ductility

Gradient materials often have attractive mechanical properties that outperform uniform microstructure counterparts. It remains a difficult task to investigate and compare the performance of various gradient microstructures due to the difficulty of fabrication, the wide range of length scales involved, and their respective volume percentage variations. We have investigated four types of gradient microstructures in 304 stainless steels that utilize submicrotwins, nanotwins, nanocrystalline-, ultrafine- and coarse-grains as building blocks. Tensile tests reveal that the gradient microstructure consisting of submicrotwins and nanotwins has a persistent and stable work hardening rate and yields an impressive combination of high strength and high ductility, leading to a toughness that is nearly 50% higher than that of the coarse-grained counterpart. Ex- and in-situ transmission electron microscopy indicates that nanoscale and submicroscale twins help to suppress and limit martensitic phase transformation via the confinement of martensite within the twin lamellar. Twinning and detwinning remain active during tensile deformation and contribute to the work hardening behavior. We discuss the advantageous properties of using submicrotwins as the main load carrier and nanotwins as the strengthening layers over those coarse and nanocrystalline grains. Furthermore, our work uncovers a new gradient design strategy to help metals and alloys achieve high strength and high ductility.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Bayesian optimization with active learning of design constraints using an entropy-based approach

Abstract The design of alloys for use in gas turbine engine blades is a complex task that involves balancing multiple objectives and constraints. Candidate alloys must be ductile at room temperature and retain their yield strength at high temperatures, as well as possess low density, high thermal conductivity, narrow solidification range, high solidus temperature, and a small linear thermal expansion coefficient. Traditional Integrated Computational Materials Engineering (ICME) methods are not sufficient for exploring combinatorially-vast alloy design spaces, optimizing for multiple objectives, nor ensuring that multiple constraints are met. In this work, we propose an approach for solving a constrained multi-objective materials design problem over a large composition space, specifically focusing on the Mo-Nb-Ti-V-W system as a representative Multi-Principal Element Alloy (MPEA) for potential use in next-generation gas turbine blades. Our approach is able to learn and adapt to unknown constraints in the design space, making decisions about the best course of action at each stage of the process. As a result, we identify 21 Pareto-optimal alloys that satisfy all constraints. Our proposed framework is significantly more efficient and faster than a brute force approach.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Finding new perovskite halides via machine learning

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach toward rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning, henceforth referred to as ML) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX 3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br, or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 185 experimentally known ABX 3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor, and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. As a result, the trained and validated models then predict, with a high degree of confidence, several novel ABX 3 compositions with perovskite crystal structure.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Reduced-order modeling through machine learning and graph-theoretic approaches for brittle fracture applications

Typically, thousands of computationally expensive micro-scale simulations of brittle crack propagation are needed to upscale lower length scale phenomena to the macro-continuum scale. Running such a large number of crack propagation simulations presents a significant computational challenge, making reduced-order models (ROMs) attractive for this task. Here, the ultimate goal of this research is to develop ROMs that have sufficient accuracy and low computational cost so that these upscaling simulations can be readily performed. However, constructing ROMs for these complex simulations presents its own challenge. Here, we present and compare four different approaches for reduced-order modeling of brittle crack propagation in geomaterials. These methods rely on machine learning (ML) and graph-theoretic algorithms to approximate key aspects of the brittle crack problem. These methods also incorporate different physics-based assumptions in order to reduce the training requirements while maintaining accurate physics as much as possible. Results from the ROMs are directly compared against a high-fidelity model of brittle crack propagation. Further, the strengths and weaknesses of the ROMs are discussed, and we conclude that combining smart physics-informed feature engineering with highly trainable ML models provides the best performance. The ROMs considered here have computational costs that are orders-of-magnitude less than the cost associated with high-fidelity physical models while maintaining good accuracy.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Phase‐Change‐Memory Process at the Limit: A Proposal for Utilizing Monolayer Sb 2 Te 3

Abstract One central task of developing nonvolatile phase change memory (PCM) is to improve its scalability for high‐density data integration. In this work, by first‐principles molecular dynamics, to date the thinnest PCM material possible (0.8 nm), namely, a monolayer Sb 2 Te 3 , is proposed. Importantly, its SET (crystallization) process is a fast one‐step transition from amorphous to hexagonal phase without the usual intermediate cubic phase. An increased spatial localization of electrons due to geometrical confinement is found to be beneficial for keeping the data nonvolatile in the amorphous phase at the 2D limit. The substrate and superstrate can be utilized to control the phase change behavior: e.g., with passivated SiO 2 (001) surfaces or hexagonal Boron Nitride, the monolayer Sb 2 Te 3 can reach SET recrystallization in 0.54 ns or even as fast as 0.12 ns, but with unpassivated SiO 2 (001), this would not be possible. Besides, working with small volume PCM materials is also a natural way to lower power consumption. Therefore, the proposed PCM working process at the 2D limit will be an important potential strategy of scaling the current PCM materials for ultrahigh‐density data storage.

2D limit↗

DOE OSTI2019

Synthesis and Properties of Poly-Ether/Ethylene Carbonate Electrolytes with High Oxidative Stability

New electrolyte designs are necessary for overcoming multiple challenges posed by emerging lithium-metal battery chemistries. Task-specific viscoelastic electrolytes are specifically required that are capable of simultaneously sustaining reversible electrochemistry at the Li metal anode, maintaining high oxidative stability at the battery cathode and achieving high room-temperature ionic conductivity in the electrolyte bulk. Here, we synthesize and study a series of polymeric electrolytes composed of poly-1,3-dioxolane (poly-DOL) networks formed by ring-opening polymerization of 1,3-dioxolane/ethylene carbonate (EC) mixtures by a commonly used LiPF 6 electrolyte salt. The complex kinetics of the DOL polymerization reaction in EC results in nonlinear phase behavior, including the appearance of a critical transition to an entangled, solid-like electrolyte state at 40% DOL. Additionally, the transition is mediated by a range of physicochemical regimes—from liquid-like solutions to highly viscous conducting gels. We show that poly-DOL/EC electrolytes with a high concentration of the DOL precursor exhibit improved electrochemical stability compared to conventional DOL-based electrolytes, while maintaining good room temperature ionic conductivity. We show further that the EC component imparts oxidative stability, enabling their use in rechargeable, room-temperature Li||LiNi 0.6 Co 0.2 Mn 0.2 O 2 electrochemical cells.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

Abstract Analyzing large X-ray diffraction (XRD) datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries. Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties. Here, we report a new method for pattern analysis and phase extraction of XRD datasets. The method expands the Nonnegative Matrix Factorization method, which has been used previously to analyze such datasets, by combining it with custom clustering and cross-correlation algorithms. This new method is capable of robust determination of the number of basis patterns present in the data which, in turn, enables straightforward identification of any possible peak-shifted patterns. Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries. Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns, which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions. The process can be utilized to determine accurately the compositional phase diagram of a system under study. The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.

36 MATERIALS SCIENCE↗

DOE OSTI2019

CFD Study of Countercurrent Flow in Triply Periodic Minimal Surfaces with CO 2 BOL Solvent

Triply periodic minimal surfaces (TPMS) are promising structures for heat exchangers and ultrafiltration due to their high heat and mass transfer efficiency compared to traditional devices. Because of this, the TPMS structures are a potential candidate for building the multifunctional, intensified devices in CO 2 capture tasks. With the Lawrence Livermore National Laboratory’s (LLNL’s) additive manufacturing (AM) capability, these complicated TPMS structures can be printed in the packed column for experimental tests. To optimize the TPMS structure and enable fast prototyping, a computationally-efficient computational fluid dynamics (CFD) framework was established to explore the TPMS structure’s hydrodynamics and mass transfer performance. The established model was able to simulate the countercurrent flows by integrating the Pacific Northwest National Laboratory (PNNL) CO 2 binding organic liquids (CO 2 BOL) solvent into the TPMS structures. Three typical TPMS structures - namely, Gyroid, Schwarz-D, and Schwarz-P - were investigated using the periodic boundary set up in all three directions. Two different unit cell size (1 cm and 2 cm) geometries are tested for each type of TPMS. The solvent flow rate in this study covered a range of [0, 0.1] m/s with an initial liquid film thickness of [0.1, 1.8] mm. These simulations provided a preliminary understanding of the TPMS structure behaviors in countercurrent flow conditions. The study also related the TPMS geometry requirement to the given CO 2 BOL solvent physical property and covered reasonable packed column operation ranges.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Density Functional Theory Investigation of the NiO@Graphene Composite as a Urea Oxidation Catalyst in the Alkaline Electrolyte

Developing efficient and low-cost urea oxidation reaction (UOR) catalysts is a promising but still challenging task for environment and energy conversion technologies such as wastewater remediation and urea electrolysis. In this work, NiO nanoparticles that incorporated graphene as the NiO@Graphene composite were constructed to study the UOR process in terms of density functional theory. The single-atom model, which differed from the previous heterojunction model, was employed for the adsorption/desorption of urea and CO 2 in the alkaline media. As demonstrated from the calculated results, NiO@Graphene prefers to adsorb the hydroxyl group than urea in the initial stage due to the stronger adsorption energy of the hydroxyl group. After NiOOH@Graphene was formed in the alkaline electrolyte, it presents excellent desorption energy of CO 2 in the rate-determining step. Electronic density difference and the d band center diagram further confirmed that the Ni(III) species is the most favorable site for urea oxidation while facilitating charge transfer between urea and NiO@Graphene. Moreover, graphene provides a large surface for the incorporation of NiO nanoparticles, enhancing the electron transfer between NiOOH and graphene and promoting the mass transport in the alkaline electrolyte. Notably, this work provides theoretical guidance for the electrochemical urea oxidation work.

25 ENERGY STORAGE↗

DOE OSTI2019

Tritium Aging of Regenerated LANA.75

Tritium Aging of Regenerated LANA.75 is a multi-year project. Activities include: tritium aging of a LANA.75 sample regenerated in 2018, regenerating a second tritium-aged LANA.75 sample, and verifying the thermal stability of LANA.75 under regeneration conditions. During FY19, the Task Technical Plan (TTP) addendum providing testing guidance for monitoring tritium aging of the regenerated LANA.75 sample was approved. A desorption isotherm at 150 °C was collected on the regenerated LANA.75 sample. A second desorption isotherm at 150 °C is scheduled to be collected along with additional desorption isotherms at 80 and 120 °C. Fabrication, pressure testing, and leak testing of the high temperature test cell for regeneration of the second tritium-aged LANA.75 sample was completed. Selection of the second LANA.75 sample to be regenerated is anticipated in FY20. Thermal stability testing of LANA.75 material was completed. Hydrogen desorption isotherms at 80, 100 and 120 °C were collected prior to and after a lengthy annealing process (200 hours combined over two weeks) at 750 °C. The isotherms showed similar performances for all temperatures. However, the plateau region for the isotherms after the annealing process were flatter, suggesting that the annealing process was beneficial to the hydride. Sample analyses, including XRD, SEM, ICP and particle size distribution, are ongoing.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗